The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma

Qian Shen ¹ Cong Xiang ² Yongliang Han ³ Kui Huang ^4* Yongmei Li ^3*

• ¹ Department of Radiology, The Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, China
• ² Chongqing University of Technology, Chongqing, China
• ³ Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
• ⁴ the Affiliated Stomatology Hospital of Southwest Medical University, Luzhou, China

Objective: Computed tomography (CT) imaging of parotid pleomorphic adenoma (PA) has been widely reported, nonetheless few reports have estimated the capsule characteristics of PA at length. This study aimed to establish and validate CT-based intratumoral and peritumoral radiomics models to clarify the characteristics between parotid PA with and without complete capsule.Methods: In total, data of 129 patients with PA were randomly assigned to a training and test set at a ratio of 7:3. Quantitative radiomics features of the intratumoral and peritumoral regions of 2 mm and 5 mm on CT images were extracted, and radiomics models of Tumor, External2, External5, Tumor + External2, and Tumor + External5 were constructed and used to train six different machine learning algorithms. Meanwhile, the prediction performances of different radiomics models (Tumor，External2，External5，Tumor+External2，Tumor+External5) based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared. The receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the prediction performance of each model.Results: Among all the established machine learning prediction radiomics models, the model based on a three-phase combination had better prediction performance, and the model using a combination of intratumoral and peritumoral radiomics features achieved a higher AUC than the model with only intratumoral or peritumoral radiomics features, and the Tumor+External2 model based on LR was the optimal model, the AUC of the test set was 0.817[95%CI=0.712, 0.847], and its prediction performance was significantly higher (p < 0.05, DeLong’s test) than that with the Tumor model based on LDA (AUC of 0.772), the External2 model based on LR (AUC of 0.751), and the External5 model based on SVM (AUC of 0.667). And the Tumor+External2 model based on LR had a higher AUC than the Tumor+External5 model based on LDA (AUC = 0.817 vs. 0.796), but no statistically significant difference (P=0.667). Conclusions: The intratumoral and peritumoral radiomics model based on multiphasic CT images could accurately predict capsular characteristics of parotid of PA preoperatively, which may help in making treatment strategies before surgery, as well as avoid intraoperative tumor spillage and residuals.